.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples_gallery/plot_simulation_game.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_gallery_plot_simulation_game.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_gallery_plot_simulation_game.py:


===============
Simulation game
===============

A high quality simulation should be indistinguishable from observed data.
Can you guess where the training data ends and the simulation begins?

.. GENERATED FROM PYTHON SOURCE LINES 10-105



.. image-sg:: /examples_gallery/images/sphx_glr_plot_simulation_game_001.png
   :alt: STATFJORD - Where does the simulation start?
   :srcset: /examples_gallery/images/sphx_glr_plot_simulation_game_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd

    from dca.adca.well import Well
    from dca.datasets import load_monthly_sodir_production

    # Choose a field and whether or not to show the answer in the plot
    FIELD = "STATFJORD"
    SHOW_ANSWER = False


    # Read data from a public source (SODIR - sokkeldirektoratet)
    df = (
        load_monthly_sodir_production()
        .loc[lambda df: df.prfInformationCarrier == FIELD, :]
        .rename(
            columns={
                "prfInformationCarrier": "field",
                "prfYear": "year",
                "prfMonth": "month",
                "prfPrdOilNetMillSm3": "production",
            }
        )
        .assign(day=1)
        .assign(
            time=lambda df: pd.to_datetime(df[["year", "month", "day"]]).dt.to_period(
                freq="M"
            )
        )
        .loc[lambda df: df.production > 0]
    )


    # Create a well
    well = Well(
        production=df.production.to_numpy(),
        time=df.time,
        id=FIELD,
        preprocessing="producing_time",
        curve_model="arps",
    )


    # Fit the well
    SPLIT = 0.5
    HALF_LIFE = 24
    well = well.fit(half_life=HALF_LIFE, prior_strength=1, split=SPLIT)

    # Get arrays to simulate on and compute prev_eta on
    (x_train, y_train, w_train), (x_test, y_test, w_test) = well.get_train_test(split=SPLIT)
    preds_train = well.predict(x_train, time_on=w_train, q=0.5)

    # Prev eta is how far off the most recent training data prediction is
    prev_eta = (np.log(y_train) - np.log(preds_train))[-1]

    # Simulate
    simulations = well.simulate(
        x_test,
        time_on=w_test,
        prev_eta=prev_eta,
        seed=42,
        simulations=9,
    )


    # Create plot
    fig, ax = plt.subplots(figsize=(6, 3))
    ax.set_title(f"{FIELD} - Where does the simulation start?")

    ax.plot(x_train, y_train, color="black")

    # If the answer is to be shown, plot the decline curve
    if SHOW_ANSWER:
        x, y, w = well.get_curve_data()
        preds = well.predict(x, time_on=w, q=0.5)
        i = well.get_split_index(split=SPLIT) - HALF_LIFE * 3
        ax.plot(x[i:], preds[i:], color="orange", zorder=10, label="DCA curve")


    # Plot a single simulation (change to plot several)
    for j in range(1):
        color = None if SHOW_ANSWER else "black"
        label = "Simulation" if SHOW_ANSWER else None
        ax.plot(x_test, simulations[j, :], color=color, zorder=5, label=label)


    ax.set_ylabel("log (production rate)")
    ax.set_xlabel("Months since production start")
    ax.grid(True, ls="--", alpha=0.33)
    if SHOW_ANSWER:
        ax.legend()
    fig.tight_layout()
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.421 seconds)


.. _sphx_glr_download_examples_gallery_plot_simulation_game.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_simulation_game.ipynb <plot_simulation_game.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_simulation_game.py <plot_simulation_game.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_simulation_game.zip <plot_simulation_game.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_